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					MVS 110 EXERCISE PHYSIOLOGY

LECTURE 1 UNDERSTANDING SCIENCE

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READING #1 INTRODUCTION TO SCIENCE: THE SCIENTIFIC METHOD AND EXERCISE PHYSIOLOGY
As the academic discipline of exercise physiology emerged, so also developed research strategies for objective measurement and problem solving, and the need to report discoveries of new knowledge. For the beginning exercise physiology student, familiarization with the methods of science helps to separate fact from “hype” - most often encountered in advertising about an endless variety of products sold in the health, fitness, and nutrition marketplace. How does one really know for sure whether a product really works as advertised? Does warming up really “warm” the muscles to prevent injury or enhance subsequent performance? Will breathing oxygen on the sidelines during a football game really help the athlete recover? Do vitamins “supercharge” energy metabolism during exercise? Will creatine, chromium, or vanadium supplements add muscle mass during resistance training? Understanding the role of science in problem solving can help to make informed decisions about these and many other questions. The following section examines the goals of science, including different aspects of the scientific method of problem solving.

General Goals of Science
The two distinct goals of science often seem at odds. One goal aims to serve mankind, to provide solutions to important problems, and improve life’s overall quality. This view of science, most prevalent among nonscientists, maintains that all scientific endeavors should exhibit practicality and immediate application. The opposing view, predominant among scientists, maintains that science should describe and understand all occurrences without necessity for practical application - understanding phenomena becomes a worthy goal in itself. The desire for full knowledge implies being able to:  Account for (explain) behaviors or events  Predict (and ultimately control) future occurrences and outcomes. Regardless of one’s position concerning the goal of science, its ultimate aims include:  Explanation  Understanding  Prediction  Control

Hierarchy in Science
Full appreciation of science requires understanding its structure and three levels of conceptualization (see figure 1):  Finding facts  Developing laws  Establishing theories

Fact Finding
The most fundamental level of scientific inquiry requires the systematic observation of measurable (empirical) phenomena. Often referred to as fact-finding, this process requires
Figure 4. Foundations of science: facts, laws and theories.

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standardized procedures and levels of agreement about what constitutes acceptable observation, measurement, and data recording procedures. In essence, fact-finding involves recording information (data) about the behavior of objects. While facts provide the “building blocks” of science, the uncovering of facts represents only the first level in the hierarchy of scientific inquiry. Fact gathering occurs in many ways. We usually observe phenomena through visual, auditory, and tactile sensory input. Regardless of the observation method, to establish something as fact demands that different researchers reproduce observations under identical conditions on different occasions. For example, the healthy human heart’s four chambers and the average sea level barometric pressure of 760 mm Hg represent indisputable, easily verifiable “facts.” Facts usually take the form of objective statements about the observation such as: “Jesse’s body mass measured on a balance scale equals 70 kg (154 lb.), or “Jesse’s heart rate upon rising following eight hours of sleep averages 63 beats per minute.”

FOR YOUR INFORMATION FACTS ARE FACTS…
Facts exhibit no moral quality; once established, any question about facts arises only from interpretation. While some may disagree with the meaning and implications of an established fact (e.g., the average woman possesses 50% of absolute upper body strength of a male counterpart), no question exists about the “correctness” of the observation (that women have less upper body strength than males). In essence, a fact is a fact....

Interpreting Facts
Fact-finding evaluates the observed object, occurrence, or phenomenon along a continuum, either imagined or real that represents its underlying measurable “dimension.” The term variable identifies this measurable characteristic. Frequently, quantification of the variable results from assigning numbers to objects or events to describe their properties. For example, consider the variable percent body fat with numerical values ranging from 3 to 60% of total body mass. Other examples include the weight of an object along a “heaviness” continuum, order of team finish in the NFL's American Conference, or heart rate from rest to maximal exercise. Some variables like 50-m swim time or blood cholesterol level distribute in a continuous nature; they can take on any numerical value, depending on the precision of the measuring instrument. Continuous variables can further categorize into ordinal, interval, and ratio numerical data. Ordinal variables have rank-ordered values (e.g., small, medium, large bone frame size; first through tenth place finish in a race; standings in league competition) according to some property about each person, group, object, or event compared to others studied. In ordered ranking, no inference exists of equal differences between specific ranks (e.g., race time difference between first and second place finish equals difference between ninth and tenth place). Interval variables exhibit similar properties as ordinal variables, except the distance between successive values on an unbroken scale from low to high represents the same amount of change. For example, in marathon running, the temporal 20-minute difference between a finish time of 2 hr: 10 min and 2 hr: 30 min equals that of 3 hr: 50 min and 4 hr: 10 min. The ratio scale possesses properties of interval and ordinal scoring, but also contains an absolute zero point. Thus, a variable scored on a ratio basis with a value of 4 represents twice as much characteristic as a value of 2; this does not occur with interval-scored variables like temperature where 30°F is not twice as “hot” as 15°F. In addition to continuous variables, some variables possess discrete properties. Scores for discrete variables fall only at certain points along a scale, like scores in most sporting events - “almost in” does not count in golf, soccer, basketball, or lacrosse. Discrete variables occur when the score’s value simply reflects some characteristic of the object (e.g., male or female, hit or miss, win or lose, or true or false).

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Casual and Causal Relationships
A fundamental scientific process involves observing and objectively measuring the quantity of a variable. However, it sometimes becomes more important to consider how data from one variable relate to data from another variable. Understanding how variables change in relation to each other represents a higher level of science than merely describing and quantifying diverse isolated variables. For example, quantifying the degree of association between maximal oxygen uptake capacity (abbreviated VO 2max) and chronological age reflects a higher level of understanding than describing the “facts” concerning each variable separately. An extreme example to illustrate that association between variables does not necessarily infer causality considers the strong direct association in western culture between the length of one’s trousers and stature (i.e., taller individuals wear longer-length pants than shorter counterparts). It seems highly unlikely that increasing trouser length would increase stature! In reality, this association is casual, not causal, being driven more by cultural mores that “require” trousers to descend to ankle level - and leg length relates closely with overall body stature. The well established positive relationship between increasing age and increasing systolic blood pressure among adults does not necessarily mean that one should expect to inevitably become hypertensive with advancing years. Rather, the relationship exists between aging and blood pressure because other factors sedentary lifestyle, obesity, arteriosclerosis, increased stress, and poor diet - often increase with age. Each of these variables independently can elevate blood pressure. From a scientific perspective, a change in one variable (X) does not necessarily cause changes in the other variable(Y), simply because X and Y relate in a manner that seems to “makes sense.”

FOR YOUR INFORMATION CAUSALITY AND SCIENCE
To infer causality, science requires that a change in the X-variable (independent manipulated variable) precedes a change in the Y-variable (dependent variable expected to change), with consideration, accounting for, or control of other variables that might actually cause the relationship. Understanding causal factors in relationships among variables enhances one’s understanding about observed facts. INDEPENDENT AND DEPENDENT VARIABLES Two categories of variables, independent and dependent, take on added importance when defining the nature of relationships among occurrences. This categorization relates to the manner of the variable’s use, not the nature of the variable itself. For causal relationships, manipulation of the value of the independent variable (X-variable) changes the value of the dependent variable (Y-variable). For example, increases in dietary intake of saturated fatty acids (independent X-variable) increase levels of serum cholesterol (dependent Y-variable), while decreases in saturated fatty acid intake reduce serum cholesterol levels. In other words, the value of the dependent variable literally “depends upon” the value of the independent variable. For noncausal relationships, the distinction between dependent and independent variables becomes less clear. In such cases, the independent variable (e.g., the sum of five skinfolds or recovery heart rate on a step test) usually becomes the predictor variable, while the dependent variable (percent body fat or maximal oxygen uptake) represents the quality predicted. In some cases, an independent variable becomes the dependent variable, and vice versa. For example, body temperature represents the independent variable when used to predict change in regional blood flow or sweating response; body temperature assumes a dependent variable role when evaluating effectiveness of thermoregulation during heat stress. ESTABLISHING CAUSALITY BETWEEN VARIABLES Scientists attempt to establish cause and effect relationships between independent and dependent variables by one of two methods:

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 Experimental studies  Field studies NATURE OF EXPERIMENTAL STUDIES An experiment represents a set of operations to determine the underlying nature of the causal relationship between independent and dependent variables. Systematically changing the value of the independent variable and measuring the effect on the dependent variable characterizes experimentation. In some cases, the experiment evaluates the effect of combinations of independent variables (e.g., anabolic steroid administration plus resistance training; pre-exercise warm-up plus creatine supplementation) relative to one or more dependent variables. Regardless of the number of variables studied, an experiment’s ultimate goal attempts to systematically isolate the effect of at least one independent variable in relation to at least one dependent variable. Only when this occurs can one decide which variable(s) really explains the phenomenon. NATURE OF FIELD STUDIES Field studies mostly investigate events as they occur in normal living. Under such “natural” conditions, it becomes impossible to experimentally vary the independent variable, or exert full control over potential interacting factors that might affect the relationships. In medical areas, field studies (termed epidemiological research) investigate the characteristics of a group as they relate to the risks, prevalence, and severity of specific diseases. To a large extent, “risk profiles” for coronary artery disease, various cancers, and AIDS have emerged from associations generated from field studies. In exercise physiology, a field study might involve collecting data during a “real world” test of a new piece of exercise equipment, as shown in Figure 2. In this field experiment the subject wears a wristwatch that receives signals from a chest strap transmitter that sends the heart's electrical signals to the watch. The subject then pedals the “Surfbike” at different speeds to estimate heart rate during different exercise durations. Prior to the aquatic experiments, the subject’s heart rate and oxygen uptake were determined in the laboratory while pedaling a bicycle ergometer at different speeds. A linear relationship between laboratory determined heart rate and oxygen uptake allowed the researcher to “predict” the subject's oxygen uptake from heart rate measured during Surfbike exercise. An estimate of oxygen uptake permits calculation of caloric expenditure. In this particular experiment, Surfbike exercise at a heart rate of 178 beats per minute translated to 10.4 calories expended per minute. While field studies provide objective insight about possible causes for observed phenomena, the lack of full control inherent in such research limits their ability to infer causality. Because neither active manipulation of the independent variable by the experimenter nor control over potential intervening factors occurs, no certainty exists that any observed variation in the dependent variable will result from variations in the independent variable.

Establishing Laws
Fact gathering generally does not generate much controversy; after all, facts are facts! Interpretation of facts, however, raises science to a level rife for debate. Interpreting facts leads to the second level of the scientific process creating statements that describe, integrate, or summarize facts and observations. Such statements are known as laws. More precisely, a law represents a statement describing the relationships among independent and dependent variables. Laws generate from inductive reasoning (moving from specific facts to general principles). Many examples of laws exist in physiology. For example, blood flows through the vascular circuit in general accord with the physical laws of hydrodynamics applied to rigid, cylindrical vessels. Although true only in a qualitative sense when applied to the body, one law of
Figure 2. Field study in exercise to estimate energy expenditure individuals pedaling a “surfbike”.

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hydrodynamics, termed Poiseuille's law, describes the interacting relationships among a pressure gradient, vessel radius, vessel length, and fluid viscosity on the force impeding blood flow. Laws are purposely not very specific; thus, they remain powerful because they generalize to many different situations. One variation of Hooke’s law of springs, made in 1678 by Robert Hooke (1635-1703), a contemporary of sir Issac Newton, states that elongation of a spring relates in direct proportion to the force needed to produce the elongation. Engineers apply this law to design springs for different kinds of instruments via simple calculations in accordance with Hooke’s law. A good (useful) law accounts for all of the facts among variables. Many laws have limits because they apply to only certain situations. A limited law proves less useful in predicting new facts. A fundamental aspect of science tests predictions generated from a particular law. If the prediction holds up, the law expands to additional situations; if not, the law becomes restated in more restrictive terms. Developing new technologies often permits testing laws in situations heretofore thought impossible; this allows for development of a more comprehensive law. Laws do not provide an explanation why variables behave the way they do; laws only provide a general summary of the relationship among variables. Theories explain the how and whys about a laws.

Developing Theories
Theories attempt to explain the fundamental nature of laws. Theories offer abstract explanations of laws and facts. They try to explain the “why” of laws. Theories involve a more complex understanding (and explanation) of variables than do laws. Examples of theories include Darwin's theory of natural selection and evolution, Einstein's theory of relativity, Canon's theory of emotions, Freud's theory of personality formation and development, and Helmholtz's theories of color vision and hearing. Theories consist of three aspects: 1. Hypothetical construct Hypothetical constructs represent non-observable abstract entities, consciously invented and generalized for use in theories. For example, the construct of “intelligence” emerged from observations of presumably intelligent and non-intelligent behaviors. “Physical fitness” represents another common construct in areas related to exercise physiology. 2. Associations among constructs Scientific inquiry often requires defining relationships among constructs. For example, the construct “physical ability” becomes clarified by its association to the construct “physical fitness,” which itself becomes operationally defined (see below) by numerous specific “fitness” tests. In essence, the meaning of one construct becomes understood through its relationship to other more clearly defined constructs. 3. Operational definitions The scientific process requires refinement of constructs into observable characteristics for objective quantification and recording. Operational definitions assign meaning to a construct by clearly outlining the set of operations (like an instruction manual) to measure the quantity of that construct or to manipulate it. For example, the construct intelligence only becomes understood when operationally defined (score on a specific IQ test).

The Surety of Science
Experimentation represents the scientific mechanism for testing hypothesis; scientists either reject or fail to reject an hypothesis. Rejecting a hypothesis represents a powerful outcome because it may nullify a theory and specific predictions generated from the theory. Failure to reject an hypothesis indicates that the observable results appear to support the theory. The terms reject and fail to reject (in contrast to prove and disprove) deserve special attention. Failure to reject does not indicate confirmation or proof, only inability to

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reject an hypothesis. However, if other experiments (particularly from independent laboratories) also fail to reject a given hypothesis, a strong likelihood exists (high probability) of a correct hypothesis. The structure of science makes it impossible to totally confirm a theory's absolute “correctness” because scientists may still devise a future experiment to disprove the theory. The strength of the experimental method lies in rejecting hypotheses that have direct bearing on theories or predictions from theories. The notion of disproof represents an important distinguishing feature of the scientific method.

Publishing Results of Experiments
Fact-finding, law formulation, and theory development represent fundamental aspects of science. Allowing fellow scientists to critique one’s research findings prior to their distribution completes the process of scientific inquiry. Most journals that disseminate research rely on the researcher’s peers to review and pass judgment on the suitability and quality of methods, experimental design, appropriateness of conclusions, and contribution to new knowledge. While this aspect of science often receives criticism for failing to achieve true objectivity and freedom from professional bias, few would discount its importance; when executed properly, peer review in refereed journals maintains a level of “quality control” in disseminating new information. Imagine the many instances where an experimental outcome could be influenced by self-interest and/or professional bias. Athletic shoe and nutrient supplement manufacturers sponsor sophisticated laboratories to conduct detailed “research” on the efficacy of their products. To assure credibility, research from such laboratories must be reviewed by experts having no affiliation (direct or indirect) with the company. Without a system of “checks and balances,” such studies should be rightfully viewed with skepticism, and lack trustworthiness as a legitimate source of new knowledge.

Empirical vs. Theoretical – Basic vs. Applied Research
Different approaches lead to successful experimentation and knowledge acquisition. Figure 3 shows two different continuum for experimentation. The theoretical-empirical research continuum has at its foundation experimentation related to establishing laws and testing theories. Scientists in theoretical research maintain that fact finding alone represents an unfocused waste of energy if the process does not emanate from and contribute to theory building. Scientists at the opposite end of the continuum collect facts and make observations with little regard for building theory. The influential psychologist B.F. Skinner exemplifies the proponent of the empirical research (experience related) approach. His discoveries about reinforcement - a reward for successful behavior increases the probability of success in subsequent Figure 3. Research continuum in science. trials - were uncovered by “accident.” Skinnerian empiricists argue that theoretical scientists often do not uncover meaningful relationships because they become too “locked into” theoretical formulations and abstract models.

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Basic-applied research represents another continuum. Applied research incorporates scientific endeavors to solve specific problems, the solution of which directly applies to medicine, business, the military, sports performance, or society’s general well being. Applied research in exercise physiology might focus on methods for improving training responsiveness, facilitating fluid replenishment and temperature regulation in exercise, enhancing endurance performance, blunting the effects of fatigue by-products, and countering the deterioration of physiologic function during prolonged exposure to a weightless environment. Basic research lies at the other end of this continuum; no concern exists for immediate practical application of research findings. Instead, the researcher pursues a line of inquire purely for the sake of discovering new knowledge. Often times, uncovering facts that initially seem of little value fill a theoretical void – and like magic, a wonderful new practical solution (or product) emerges. Nowhere has this taken place with more regularity than with research related to the space program. Facts uncovered in a weightless environment about fundamental biological and chemical processes have contributed to practical outcomes that benefit humans. Experiments on how certain chemicals react in zero gravity, for example, have resulted in the discovery of at least 25 new medicines. Manned space missions have provided fresh insights into almost every facet of medicine and physiology, from the affects of weightlessness on bone dynamics, blood pressure, and cardiac, respiratory, hormonal, neural, and muscular function, to growth of genetically engineered plants and a new generation of polymers. Each new insight and observation spawns numerous new ideas and additional facts that help to create products with practical applications. Research can be generally classified into one of four categories depicted by the quadrants in Figure 6. Basic-empirical research in Quadrant 1 has no immediate practical outcomes and little to do with theory. Research without immediate practical implications, but motivated by theory (establishing laws and conducting experiments that bear on theory), falls into Quadrant 2. Quadrant 3 contains theoretical-applied research primarily focused on problem solving within the framework of an existing theoretical model, while Quadrant 4 classifies empirical-applied research (not theory based), but aimed at solving problems. Often, lines of demarcation are not as clear-cut as in the figure, and a particular research effort might qualify for inclusion in multiple quadrants.


				
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